Mengchuan Wang
Post-Doctoral Fellow in Finance | Emory University, Goizueta Business School
ATLANTA, GA, UNITED STATES
Links (1)
Biography
Mengchuan (Kitty) Wang joined Goizueta Business School as a Postdoctoral Fellow in the Finance Area after completing her PhD in finance from the University of Melbourne in 2022. Before that, she obtained her dual master's degree in finance from Washington University in St. Louis and Singapore Management University and her bachelor's degree from the Renmin University of China majoring in finance. Her research interests are in fields of empirical asset pricing, fund performance evaluation, and machine learning. She is among the first to analyze mutual fund skills with machine learning techniques, which she uses to address model mis-specification problems associated with traditional studies. Her machine learning based approach identifies persistent timing skills for mutual funds and leads to a measure that can help investors to select good funds. Wang is a cat lover, and she likes to spend time meditating, singing, and dancing.
Education (4)
University of Melbourne: PhD, Finance 2022
Washington University: Master of Science, Finance 2016
Singapore Management University: Master of Science, Finance 2016
Renmin University of China: Bachelor, Finance 2015
Areas of Expertise (4)
Empirical Asset Pricing
Mutual Fund Performance Evaluation
Machine Learning
Textual Analysis
Working Papers/Projects (4)
"Disentangling Market Timing from Stock Picking---A Machine Learning Based Approach" with Michael Gallmeyer and Andrea Lu
2023-08-20
Traditional performance attribution approaches may attribute mutual fund market timing to stock picking due to new risk factors, characteristics, and industry rotations. Our theoretical framework shows that successful timing (picking) strategies entail buying stocks with high future systematic (idiosyncratic) returns. Therefore the covariance between fund holding weights and future stock systematic (idiosyncratic) returns measures timing (picking) performance. Our regression tree approach accurately distinguishes systematic from idiosyncratic returns, accommodating complexities in the return structure. Novel to the literature, funds have significant and persistent timing and picking skills. By buying past winners, investors can achieve an annual risk-adjusted return of 2.93%.
"UNexpected Returns"
2022-11-21
This paper studies the partitioning of stocks into groups with distinctive expected returns based on ex-ante firm characteristics, which can be used as comparable groups to compute the abnormal part of returns, that is, UNexpected returns. In order for stock expected returns to be similar within groups and disperse across groups, I introduce a methodology to select characteristics that best distinguish expected returns, and cutoffs points where returns are most sensitive to the underlying characteristics. I show that: 1) the combination of chosen characteristics changes over time; 2) fewer fund managers are identified to be stock pickers once the time-variation in comparable groups is incorporated; 3) and the resulting portfolios exhibit desirable properties as basis assets.
"Failure Mimicking Portfolios" with Neal Galpin
2023-11-15
Regressing a constant on a set of excess returns gives portfolio weights for the minimum-variance stochastic discount factor (SDF). We show that discounting returns by a given model, then applying the same procedure to these discounted returns gives portfolio weights to mimic SDF errors. We compare these failure mimicking portfolios} for leading consumption-based asset pricing models. Models like habits or long-run risks do not perform substantially better than the simple consumption-CAPM in the cross-section of returns. Moreover, we show that all failure mimicking portfolios load substantially on earnings-related trading strategies, suggesting avenues for future consumption-based models.
"Which Portfolios are Most Important to Asset Pricing?"
2020-01-01
We estimate a non-parametric stochastic discount factor (SDF) from a set of portfolios, then test whether excluding a portfolio changes the implied SDF. Though related to traditional asset pricing tests, our approach has several advantages: we test all portfolios jointly and can incorporate trading costs easily. We show four portfolios provide independent information about the SDF after accounting for trading costs: the Market and Profitability factors, an Investment-based portfolio, and the Value-Momentum-Profitability anomaly portfolio. The remaining portfolios are redundant. We show both the joint testing and transaction cost adjustments are important for inference, and provide a simple way to implement our tests.
